System and method of assessing intra-arterial fluid volume using intelligent pulse averaging with integrated EKG and PPG sensors

ABSTRACT

A system using combined electrocardiography (EKG) and photoplethysmography (PPG) sensing to assess intra-arterial fluid volume is described. The system uses averaging of similar pulses based on prior (n−1; n minus 1) R-to-R pulse wave duration, and prior-prior (n−2; n minus 2) R-to-R pulse wave duration, to determine a patient&#39;s fluid status and whether it is below or above optimal intravascular hydration.

RELATED APPLICATIONS

The present application claims priority to U.S. Provisional PatentApplication Ser. No. 63/009,470, entitled PULSE WAVE TRANSIT TIME (PWTT)MEASUREMENT SYSTEM USING INTEGRATED EKG AND PPG SENSORS, filed Apr. 14,2020, and to U.S. Provisional Application Ser. No. 63/067,147, entitled,SYSTEM FOR IMPROVED MEASUREMENT OF OXYGEN SATURATION, NON-INVASIVEDETECTION OF VENOUS AND ARTERIAL PULSE WAVEFORMS, AS WELL AS DETECTIONOF CARBOXYHEMOGLOBIN, HYPERTROPHIC CARDIOMYOPATHY AND OTHER CARDIACCONDITIONS, filed Aug. 18, 2020, and to U.S. patent application Ser. No.17/135,936, entitled SYSTEMS FOR SYNCHRONIZING DIFFERENT DEVICES TO ACARDIAC CYCLE AND FOR GENERATING PULSE WAVEFORMS FROM SYNCHRONIZED ECGAND PPG SYSTEMS, filed Dec. 28, 2020, the entire disclosures of whichare incorporated herein by reference in their entireties for allpurposes.

TECHNICAL FIELD OF THE INVENTION

The present system relates to cardiac sensing systems using combinedelectrocardiographic (EKG) and photoplethysmographic (PPG) sensingsystems.

BRIEF DESCRIPTION OF CLINICAL PROBLEM

Optimal hydration only makes sense within the context of the cardiacfunction of the given patient. There are many aspects to this optimalfunctioning, much of which is detailed in Appendix A. Though a fulldescription is beyond the scope of the background needed here, theessential problem is that it is often difficult to assess clinicallywhen to opt for fluid administration. Two organ systems—the lungs andthe kidneys—have differing requirements for optimal functioning. Thelungs do not work well with extra fluid, and the kidneys do not workwell when arterial flow drops. Additionally, the mortality consequencesof acute kidney failure from inadequate supply exceed that of acute lungfailure from excess fluid. Drastically simplifying the situation, thelungs must be kept “dry” while the kidneys are kept “wet”, all whilemaintaining adequate intravascular fluid needed to keep the flow ofnutrients to tissues and removal of wastes from tissues.

The cost of incorrect management is high. There are 2.5-6.5 millioncases of acute in-hospital acute kidney injury (AKI) per year, withmortality rates up to 20% and approximately $7500 added per case ofinpatient AKI. Mortality and costs are much higher for ICU cases. Whatpercentage of these cases could be avoidable is uncertain, but therecent studies done with the new Cheetah Nicom® system made by CheetahMedical of Newton Center, Massachusetts, suggest many are (mortalityreductions were not reported, but return on investment to hospitals wereover three dollars for every dollar spent on non-invasive monitoring). Ahandheld, inexpensive, easy to use, and point-of-care solution toassessing intra-arterial volume, and whether to give or remove fluidwould provide a tremendous benefit to clinicians facing difficult fluidmanagement situations.

At present, the world's health care community has battled a globalpandemic of Covid-19 disease for more than one year, commencingapproximately in January of 2020. Many hospital managers and medicalpractitioners have learned that the complex and expensive legacy medicalequipment, to include EKG and echocardiography machines, of a typicalICU, while valuable during normal times, is not well suited to the rapidtempo of an overburdened hospital ICU during a pandemic. The high costof that equipment limits availability when ubiquity is the order of theday, and complexity carries attendant burdens of painstaking andtime-consuming cleaning regimens at a time when personnel are in shortsupply. In particular, Covid patients in advanced stages frequentlyexhibit tachycardia, which limits the diagnostic value ofechocardiography.

As will be shown fully herein, the present system is not so impaired. Inparticular, the preferred embodiment presented herein providesclinically useful patient information, with added benefits of beingcomparatively low-cost, fast and simple to use, and easily cleanedbetween patient applications. Thus it is well suited to the challengesof a pandemic treatment environment.

SUMMARY OF THE INVENTION

In preferred aspects, the present system assesses intra-arterial fluidvolume with a preferred system, comprising: (a) a device positionableagainst a person's skin; (b) at least one PPG sensor mounted on thedevice for measuring the person's PPG signal at multiple wavelengths oflight; (c) a plurality of electrodes for measuring the person's EKGsignal; (d) a computer logic system for receiving and analyzing the PPGsignal and the EKG signal, wherein the computer logic system furthercomprises: (i) a system for identifying cardiac cycles in the EKGsignal; (ii) a system for segmenting the PPG signal into a series of PPGsignal segments based upon features in the identified cardiac cycles,(iii) a system for sorting the PPG signal segments into a plurality ofbins based upon durations of (a) prior R-to-R cardiac cycles and (b)prior-prior R-to-R cardiac cycles, (iv) a system for generating acomposite signal for each of the plurality of bins, and (v) a system formeasuring a person's relative hydration level by comparing the compositesignals generated from bins on the basis of the prior R-to-R cardiaccycles against the composite signals generated from bins on the basis ofthe prior-prior R-to-R cardiac cycles.

In further preferred aspects, comparing the composite signals generatedfrom bins on the basis of the prior R-to-R cardiac cycles against thecomposite signals generated from bins the basis of the prior-priorR-to-R cardiac cycles may be done by: plotting a first line representingleft ventricular output with arterial pulse shape as a function of priorR-to-R, the first line being based upon values of the composite signalsgenerated from prior R-to-R cardiac cycles, plotting a second linerepresenting venous return with arterial hemoglobin oxygen saturation asa function of prior-prior R-to-R, the second line being based upon thecomposite signals generated from prior-prior R-to-R cardiac cycles, andthen determining the intersection point of the first and second lines asa metric of a person's relative hydration level. In addition to simpleline plotting, however, comparing the composite signals generated frombins on the basis of the prior R-to-R cardiac cycles against thecomposite signals generated from bins the basis of the prior-priorR-to-R cardiac cycles may be done by: calculating a first relationshiprepresenting left ventricular output with arterial pulse shape as afunction of prior R-to-R, the first relationship being based upon valuesof the composite signals generated from prior R-to-R cardiac cycles,calculating a second relationship representing venous return witharterial hemoglobin oxygen saturation as a function of prior-priorR-to-R, the second relationship being based upon the composite signalsgenerated from prior-prior R-to-R cardiac cycles, and then comparing thefirst and second relationships as a metric of a person's relativehydration level.

In preferred aspect, the present system measuring a person's hydrationlevel by detecting changes in the shape of a composite signal measuredat an infrared wavelength of light by correlating intra-arterial fluidvolume to the area under the curve of the composite signal measured atan infrared wavelength of light.

In preferred aspects, the system for generating a composite signal foreach bin comprises a system for summing or averaging the PPG signalsegments in the bin, and the composite signal is used to generate acomposite Signal Prime Over Signal (SPOS) being the derivative of thecomposite signal normalized by the composite signal itself.

In various preferred physical embodiments illustrated herein, thepresent system is a hand-held device with the at least one PPG sensormounted thereon and a plurality of electrode wires extending therefromor mounted thereon. Alternatively, the present system may be positionedwithin a strap or band disposed around the person's chest or limb withat least one PPG sensor and the plurality of electrodes are disposedwithin the strap or band. Alternatively, the present system may bedisposed in a patch with the at least one PPG sensor and at least one ofthe plurality of electrodes positioned therein.

System also provided for data transmission. Further systems are providedfor iteratively removing aberrant PPG signal segments from thecalculation of the composite signal.

The present system provides information regarding the intra-arterialfluid status. Such knowledge allows clinicians to know when to give, orremove, fluid. At heart is the analysis shown in the system top-levelflow diagram of FIG. 1 resulting in arterial oxygen saturation fractiondependency 101 and arterial shape dependency 102. Specifically, theline/relationship describing the relationship between arterial PPG pulseshape and prior pulse EKG RtoR duration, and the line/relationshipdescribing the relationship between arterial hemoglobin oxygensaturation fraction and prior-prior pulse EKG RtoR duration are firstdetermined. These two relationships allow for assessment of thecardiovascular status as in FIG. 2 . FIG. 2 shows a system top-levelflow diagram, resulting in positions located on a surrogateFrank-Starling curve, before and after a challenge of hydration (eitherIV fluids or mobilization of fluids from the lower extremities). Curve203 moves left with the fluid challenge to curve 204, and curve 205moves rightward to 206 as a result of the fluid challenge. The “X” at201 notes the pre-challenge position on the graph of the intersection ofcurves 203 and 205. The “X” at 202 notes the post-challenge position onthe graph of the intersection of curves 204 and 206. The key insight isthat movement upward from point 201 to 202 as a result of a trial ofhydration represents a beneficial response in intra-arterial volume tohydration.

As described herein, the present system uses combinedelectrocardiography (EKG) and photoplethysmography (PPG) signals. (PPGis also commonly referred to as oximetry and the two terms will be usedinterchangeably throughout this specification). The former sensesvoltage produced by heart muscle contraction, and the latter measureslight absorbed by tissues. Measurement at different wavelengths allowsdetermination of volume. Changes in PPG signals reflect changes in bloodvolume and measurement at different wavelengths allows determination ofarterial oxygen saturation.

As will be shown, the present system permits different insight than iscurrently available using existing hand-held, portable PPGsystems/devices. In the present system, the combination of EKG and PPGsignals utilize Pulse Wave Transit Time (hereafter “PWTT”), PPG SignalPrime Over Signal (hereafter “SPOS”) curves, and PPG signal segments. Asunderstood herein, a PPG signal segment means a PPG signal of any lengthshorter than, equal to, or longer than a cardiac cycle.

PWTT is the period of time taken between a heartbeat as measured by theonset of the QRS complex and the time at which the blood from the aortareaches an extremity or other body part, as determined by the negativespike generated in the SPOS curve, also described as the derivative ofthe LED signal divided by the signal. Use of the signal derivative todetermine the change in a LED signal heralding the arrival of anarterial pulse has been described in U.S. Pat. No. 10,213,123, assignedto MocaCare Corporation of Palo Alto, California. However, the presentnovel use of the signal prime over signal (SPOS) allows for greaterinsight, as it normalizes each wavelength signal and thus allows forcomparisons between different wavelength SPOS curves.

Improved arterial oxygen saturation estimation is then generated by thepresent system from an SPOS curve of a composite sum/average of similarpulses. Prior (n−1) EKG R-to-R duration using R-wave peaks arecalculated, as are prior-prior (n−2) R-to-R duration, PWTT, and SPOS.These are all used by the system to determine similarity of oximetrypulses, with similar pulses summed/averaged to form composite pulses,then compare differing composite pulses to gain cardiovascular insight.

Reduced PWTT corresponds to greater pulse wave velocity, though thegreater velocity does not indicate better pump function. This is becausethe aortic bulb acts as a “mechanical capacitor”, allowing metereddelivery of arterial pulse volume. However, having obtained the PWTT forany given monitoring point on the body, this metric remains relativelystable and changes only gradually barring a sudden change incardiovascular state (e.g. sudden change in heart rhythm such as onsetof atrial fibrillation with rapid ventricular response). PWTT thereforeprovides a means by which to ensure accurate further data collection andanalysis. This allows more reliable extraction of additional informationfrom the combination of signals, and removal/minimization of introducednoise.

Measurement of absorption of light (per Beer-Lambert law) has the formMeasurement(t)=Ke^([-cf(t)]), and the signal prime over signal (SPOS) ofthe measurement will be:

${SPOS}{(t) = {- {{C\left( \frac{d{f(t)}}{dt} \right)}.}}}$

The LED signals in plethysmography have the form:Signal=K*e ^([-Arterial(t)*Σ(α*Hb)) ^(arterial) ^(]) *e^([-Venous(t)*Σ(α*Hb)) ^(venous) ^(])  (1)

Σ(α*Hb)_(arterial) and Σ(α*Hb)_(venous) describe the composition of theblood and generally change slowly. Therefore, these two terms areconstants across time for the duration of our sampling. (These termswill be explained in greater detail below).

Further, in healthy individuals, the venous flow is considered aconstant. Current oximetry measures assume this, and this assumption wasused by the present inventors for this initial exploration. Given thisassumption, the equation reduces to:Signal=K ₁ *e[ ^(−Arterial(t)*Σ(α*Hb)) ^(arterial) ^(])  (2)

Using properties of the exponential function, and of its derivative, wederive the SPOS for the PPG Signal at several wavelengths (e.g., IR andRed).

$\begin{matrix}{{{SPOS}(t)} = {{- \left( \frac{{dArterial}(t)}{dt} \right)}*{\sum\left( {\alpha*Hb} \right)_{arterial}}}} & (3)\end{matrix}$

Using the fact that the conceptual function Arterial(t) is the same forboth Red and IR PPG Signals, we show that the SPOS of the signal fromthe IR LED (SPOS_(IR)) is directly proportional to the SPOS of thesignal from the Red LED (SPOS_(Red)):SPOS_(Red) =R*SPOS_(IR) or SPOS_(Red)/SPOS_(IR) =R  (4)

Returning to the expression

∑(α_(μ_(Hb_(x))) * Hb_(x))This describes how different wavelengths of light are absorbed by theblood depending on the relative quantity of the types of hemoglobinpresent within.

Where:

$\begin{matrix}\alpha_{\mu_{Hb_{x}}} & \;\end{matrix}$=absorption coefficient for type of hemoglobin (deoxyhemoglobin,oxyhemoglobin, carboxyhemoglobin, methemoglobin), x, for the wavelength,μ

Hb_(x)=fractional composition of blood of various types of hemoglobin.The Sum of fractional components of different types of hemoglobin=1.0

In the conditions of low levels of carboxyhemoglobin and methemoglobin(e.g. excepting situations such as carbon monoxide or cyanidepoisoning), and using accepted standard absorption coefficients forα_(IR) _(HB) , α_(Red) _(Hb) , Hb=1−Hb₀ ₂ .

This results with the equation:

$\begin{matrix}{{\left( {\alpha_{{Red}_{Hb_{o_{2}}}}*Hb_{o_{2}}} \right) + \left( {\alpha_{{Red}_{Hb}}*\left( {1 - {Hb_{o_{2}}}} \right)} \right)} = {R*\left\lbrack {\left( {\alpha_{{IR}_{Hb_{o_{2}}}}*Hb_{o_{2}}} \right) + \left( {\alpha_{{IR}_{Hb}}*\left( {1 - {Hb_{o_{2}}}} \right)} \right)} \right\rbrack}} & (5)\end{matrix}$

The only unknown is Hb₀ ₂ . Solving for Hb₀ ₂ gives us the fraction ofthe blood that is oxygenated (Arterial oxygenated hemoglobin Fraction,or Arterial Frac O2):

$\begin{matrix}{{{Arterial}\mspace{14mu}{Frac}\mspace{14mu} O_{2}} = \frac{\left( {{{- \alpha_{IRHb}}*R} + \alpha_{RedHb}} \right)}{{R*\left( {\alpha_{{{IRHb}O}_{2}} - \alpha_{IRHb}} \right)} + \left( {\alpha_{RedHb} - \alpha_{{{RedHb}O}_{2}}} \right)}} & (6)\end{matrix}$

This direct proportionality between SPOS for any wavelength and thesummation of optical absorption coefficients times the fraction ofhemoglobin is used extensively by the system.

Any recording of EKG, or oximetry signals, or their interaction, willhave physiologic variability, as well as noise. Management of EKG noisehave established protocols that have been built up over 100 years.Conditioning of oximetry signals do not have as long a history.Physiologic oximetry variability can occur from changes in venous flow(due to volitional movement, or passive movement from repositioning, orinflation/deflation of a blood pressure cuff/sphygmomanometer, etc.),respiration causing changes in intra-thoracic pressure with resultantchange in blood volume return to the heart, or beat-to-beat durationvariability. Noise, or non-physiologic variability, can also occur froma range of possibilities, from variation in the surface pressure andangle of application of the detector, to ambient light infection ofsignal collection, to DC drift of the detection circuit. Whatever thespecific source of variation, without an intelligent approach to thesignals, one cannot tell physiologic variability apart fromnon-physiologic variability (introduced noise).

Traditional means for dealing with noise introduced into oximetrysignals is to filter. For example, a commonly used algorithm fordetecting signal to noise ratio utilizes power within the frequenciesbelow 20 Hz compared with power above this frequency (as described inMaximIntegrated AppNote AN6410.pdf provided by Maxim IntegratedCorporation of San Jose, California). This frequency filteringhighlights the underlying primary rhythm (heart rate) and smooths theappearance of the displayed waveform. However, pulses are not all thesame, and treating them as if they are deletes valuable information thatcan be mined for deeper insight.

An alternative means by which to minimize variability is to average theoximetry over many pulses, as described in U.S. Pat. No. 10,485,433,assigned to Intel Corporation. This approach allows for minimization ofintroduced noise, but eliminates any information that could be gleanedfrom physiologic variability. This approach produces a single,homogenized, and representative pulse at the end of the process.However, pulses are not all the same, and treating them as if they areeffectively obliterates some of the available information.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a top-level flow diagram of the operation of the presentsystem.

FIG. 2 is an assessment of cardiovascular status determined by thepresent system as presented on a surrogate Frank-Starling curve.

FIG. 3 is an illustration of the Frank-Starling relationship.

FIG. 4 is an under the curve analysis of a composite infrared (IR) PPGsignal, showing changes to the curve under different patient hydrationlevels.

FIG. 5 is an illustration of “Pulse Area”, showing an area under thecurve value from a composite infrared (IR) PPG as a stand-in for they-axis on the Frank-Starling curve.

FIGS. 6A and 6B illustrate different Frank-Starling curves underdifferent patient hydration conditions.

FIG. 7 shows the dynamics of the completed surrogate Frank-Starlingcurve including surrogate venous return.

FIG. 8 shows the process of R-wave peak refinement used to generate tOn.

FIG. 9 shows the nomenclature and data structures used in thedescription of the present system.

FIG. 10 also shows the nomenclature and data structures used in thedescription of the present system.

FIG. 11 is an exemplary illustration of various physical components ofthe present system.

FIGS. 12A to 12D show various views of a hand-held embodiment of thepresent system, having PPG and EKG sensors mounted thereon or attachedthereto.

FIG. 13 is a cut-away view of a portion of the device of FIGS. 12A to12D, showing an optical waveguide adjacent to a PPG sensor.

FIG. 14A is an illustration of the system of FIGS. 12A to 13 collectingPPG signals from a person's fingers.

FIG. 14B is an illustration of the system of FIGS. 12A to 13 collectingPPG signals from the outside of a person's arm.

FIG. 15 is an illustration of a preferred method of use of the presentsystem with a positional change of the patient acting as a fluidchallenge.

FIG. 16 corresponds to FIG. 15 after the fluid challenge.

FIG. 17 is an illustration of EKG and PPG signals measured over time andgenerated SPOS signals corresponding thereto.

FIG. 18A shows a detailed plot of an arterial pulse, and FIGS. 18B to18D show PPG signals associated with this pulse at different wavelengthsof light.

FIGS. 19A to 19C illustrates calculated SPOS curves corresponding to thePPG signals of FIGS. 18B to 18D.

FIG. 20 is an illustration of one-sided Gaussian fitting.

FIG. 21 illustrates a time-correlated comparison of EKG and PPG signalsshowing the relationships in creation of multi-beat dependencies, whichare then used to create composite waves.

FIG. 22 illustrates a time-correlated comparison of EKG and PPG signalsshowing the relationships in creation of two-beat dependencies, showing“prior R-to-R” (a.k.a. “n−1 R-to-R”).

FIG. 23 illustrates a time-correlated comparison of EKG and PPG signalsshowing the relationships in creation of two-beat dependencies, showing“prior R-to-R” (a.k.a. “n−2 R-to-R”).

FIG. 24 is a flow diagram for the 2-beat dependency using Pulse Data Set“n” arterial oxygen saturation (“Arterial Frac O2”) from PPG signalswith the prior-prior R-to-R duration shown in the left limb of thediagram.

FIG. 25 is an exemplary algorithm for preparing Pulse Data Sets inaccordance with the present system.

FIG. 26 illustrates the derivation of the Pulse Wave Transit Time(PWTT).

FIG. 27 shows a flow diagram for an exemplary method of calculatingarterial oxygen saturation in accordance with the present system.

FIG. 28 shows a flow diagram for an exemplary method of calculatingpulse area in accordance with the present system.

FIG. 29 illustrates an exemplary embodiment of the present systemdisposed in a chest strap.

FIG. 30 is a sectional view through the patient corresponding to FIG. 29.

FIG. 31 illustrates an exemplary embodiment of the present systemincorporating a bicep strap with an electrode extending therefrom.

FIG. 32 is a sectional view through the patient corresponding to FIG. 31.

FIG. 33 is an illustration of Frank-Starling and venous return curves,with the intersection of the curves being an indication ofcardiovascular state.

SUMMARY OF THE CARDIAC PHYSIOLOGY GERMANE TO THE INVENTION

The Frank-Starling relationship in FIG. 3 (and Appendix A) shows thatdifferent conditions at end-diastole (the end of ventricular filling)result in different outcomes. Many elements, including intravascularfluid volume, contribute to the left ventricular end-diastolic volume;yet important factors are (1) the available intravascular volume, and(2) the time allowed for filling in the cardiac cycle, specifically thetime between the end of one ventricular contraction and the onset of thenext contraction is the time available for filling.

A number of curves are seen in FIG. 3 . Some change in the state of theheart (and thus the applicable curve) is possible within a givenindividual, as seen between the two left-most curves 301 and 302. The“normal” resting curve of 301 moves left and upward to curve 302 inexercise, a normal healthy response. However, in most cases and at rest,the stretch-to-output curve describing the heart (also described as the“myocardial contractility”) is relatively stable, barring a suddenchange in the heart muscle (as with a heart attack). With heart failure(curve 303), the left ventricular output at any degree of leftventricular filling is less than with normal myocardial contractility atrest (301). Severe myocardial depression (304) is not compatible withlife: a patient with such a heart condition will have symptoms of fluidbuild-up in the lungs even at rest. Note also that with heart failurecurves there is a peak (305) to the left ventricular output, beyondwhich further ventricular filling yields ever worse output.

If the ventricle is thickened or otherwise less stretchy than normal,less stretch and thus less volume will be seen at the end of diastole.Still, for any limited range of time factors such as the pliability ofthe ventricle and the overall vascular volume are relatively fixed andcan be treated as constant. It is notable that an echocardiogram, whichtakes data over 30 to 45 minutes (depending on the difficulty ofvisualization) also treats all cardiac attributes as fixed, even as theymay be varying over the course of data collection. A system that gathersthe needed information over seconds to a few minutes is reporting on amuch narrower time frame than an echocardiogram, and can thus report onchanges within the time it would take to carry out an echocardiogram. Asopposed to these other cardiac parameters, the time for ventricularfilling is not fixed. Thus, similar pulses will have similar ventricularfilling times. (Note: While it may be difficult to determine exactly theduration of end-systole to end-diastole, for any narrow window of timethat period will be a relatively fixed fraction of the R-peak to R-peakduration determined from the EKG corresponding to the observed oximetrysignal).

The better the filling of the ventricle, the better the volume deliveryof the ventricular contraction, until such time that the ventricle isstretched beyond the peak of the Frank-Starling curve (the essence ofcongestive heart failure). For the left heart, this volume deliverycorresponds to an area under the curve analysis of the compositeinfrared (IR) PPG signal (see FIG. 4 ). FIG. 4 shows how arterial volumechange is reflected in the area under the IR PPG curve over the courseof 20-40 minutes as fluid is absorbed from the small intestine afteringestion of oral fluids. Curve 401 shows the IR PPG signal in thedehydrated state. Region 402 shows the narrow valley (corresponding tothe narrow arterial peak) that results from the heart's inability fullyengage the “mechanical capacitor” of the aortic bulb due to lack ofavailable intravascular fluid. Curve 406 shows the IR PPG signal in thepost-rehydrated state and the widened valley 407 resulting from fullengagement of the “mechanical capacitor” of the aortic bulb. Features403, 404, and 405 show the enlarging quantified IR PPG signal area underthe curve as fluid moves into the arterial space.

Selection of similar pulses can then be done using oximetry pulses withsimilar prior (n−1) R-to-R duration which reflects the ventricularfilling time. The pulses are grouped together, and ensuring the PWTTconfirms this similarity, averaged, and an area under the curve analysisis done. This creates a surrogate Frank-Starling curve using prior (n−1)R-to-R duration a stand-in for the x-axis of the standard Frank-Starlingcurve, and an area under the curve value from a composite infrared (IR)PPG as a stand-in for the y-axis on the Frank-Starling curve, herelabeled “Pulse area” (FIG. 5 ). Curve 501 is the surrogateFrank-Starling curve. The segment measured is 503, from point 504 to505, by virtue of R-to-R variability from 506 to 507, yielding thesampling window 502.

FIG. 6A and FIG. 6B show the dynamics of this surrogate Frank-Starlingcurve. FIG. 6A shows a surrogate Frank-Starling curve 601, and thechanges seen in this curve with decreased intra-arterial fluid (602),and with increased intra-arterial fluid (603). Note that each of thecurves 601, 602, and 603 are only sampled in the R-to-R window 604. Notealso how curve 603 has been sampled at its peak. Further movement ofthis curve to the left will result in a falling Pulse area result. FIG.6B shows the effect of decreased systolic (pump strength) function, asseen with interim heart attacks between sampling curve 605 and 606. FIG.6B also shows the effect of stiffening of the heart (increased diastolicdysfunction). The curve 605 moves toward curve 607. This can be seenboth transiently with increased blood pressure, and with thickening ofthe heart with chronic hypertension and also with hypertrophiccardiomyopathy. The stiffer the heart, the harder it is for the heart torelax and fill in diastole (relaxation), and thus the harder it is forthe heart to take advantage of available fluid. As previously, thecurves are sampled in the 608 window, as defined by the R-to-Rvariability.

The same dynamics of filling and contraction are at play with the rightheart as with the left heart. Venous blood return to the right heartfills the right ventricle, and the more filling of the right ventriclein diastole (relaxation) in general the more the output of the rightventricle. However, with the right heart, (1) the better the filling andthe better the contraction, the more blood is delivered through thelungs, which (2) is seen as improved systemic oxygenation (arterialoxygen saturation) two cycles later. This means that systemic arterialvolume delivery will vary dependent on prior (n−1) R-to-R duration, andsystemic arterial oxygenation will vary dependent on prior-prior (n−2)R-to-R duration. An additional caveat is that, whereas the venous volumereturn curve can be plotted with the Frank-Starling curve and normalizedto equal height, the arterial oxygen saturation values for the surrogatevenous return curves cannot be easily normalized against the volumedelivery metric used for the surrogate Frank-Starling curve. Instead,the return volume is translated into arterial hemoglobin oxygensaturation fraction with a maximum value of 1.0 (100% saturation).

FIG. 7 shows the dynamics of the completed surrogate Frank-Starlingcurve including surrogate venous return. As opposed to the adjusted leftventricular output curves that move left with increased intra-arterialfluid (curve 701, moving to 702), the adjusted venous return curves willmove right with improved hydration (curve 703, moving to 704)—so long asleft ventricular can respond to the increased fluid with increasedoutput. Clinical position 703 therefore translates up to clinicalposition 706, corresponding to a beneficial response to fluid. Note thatthe upper end of the right y-axis is 1.0, while the lower end 707 isdetermined by the patient condition (a lower starting point expectedwith pre-existing heart or lung conditions).

With this understanding of the curves expected, extraction of similarpulses can be done on the basis of prior R-to-R or prior-prior R-to-Rduration. PPG signals with similar prior (n−1) R-to-R duration aregrouped together, and area under the curve analysis is done. PPG signalswith similar prior-prior (n−2) R-to-R duration are also groupedtogether, and the oxygen saturation (arterial oxygen saturation)compared. In the clinical setting, the final picture yields clearmovement in the operating state prior to a trial of intravenous fluidand after a trial of intravenous fluid, yielding needed data regardingthe state of intra-arterial volume.

What is made possible is rapid, inexpensive, and point-of-careintra-arterial volume assessment, easily done at beside with minimaloperator training.

DETAILED DESCRIPTION OF THE INVENTION

The central element of the present system is the identification andmanipulation of PPG signals on the basis of prior R-to-R and prior-priorR-to-R duration. The present system then generates composite pulses fromsimilar pulses.

In accordance with preferred aspects disclosed in U.S. Provisionalpatent application 62/955,196, entitled A System For SynchronizingDifferent Devices To A Cardiac Cycle, filed Dec. 30, 2019 and in U.S.patent application Ser. No. 17/135,936, entitled SYSTEMS FORSYNCHRONIZING DIFFERENT DEVICES TO A CARDIAC CYCLE AND FOR GENERATINGPULSE WAVEFORMS FROM SYNCHRONIZED ECG AND PPG SYSTEMS, filed Dec. 28,2020, incorporated herein by reference in their entireties, the presentsystem uses a specific trigger to set time=0 for each beat (e.g. EKGR-wave peak) and then stores each pulse from this start point untilcompleting a full cycle of sensor data, such as with LED oximetrysignals from maximum to minimum and back to maximum—which will be awaveform longer than a single pulse length. The next pulse waveform willhave a t=0 at the next EKG R-wave peak, thus recording of the next beatwill start before the recording of the last pulse waveform hascompleted. In absolute terms, the time corresponding to t=0 for the nthpulse will be referred to as time t0n throughout the rest of thespecification.

FIG. 8 shows the process of R-wave peak refinement used to generate t0n.The example shows how the algorithm has determined the polarity of thiscollection to be negative (wires reversed), and thus the R-wave to benegative. The t0n of the R-wave peak is found using polynomial fitting(802) to EKG datapoints (801) and interpolation, then used to define aPulse Data Set.

FIG. 9 and FIG. 10 show the nomenclature and data structures used in thedescription of the present system. (Unless otherwise specified,PWTT=PWTTIR and PPG signal=PPG signalIR). The t0n time point is thenused to define a Pulse Data Set with the PPG signals of multiplewavelengths (here red, infrared, and green). Stored with the PPG signalare the values for the prior R-to-R, and prior-prior R-to-R durations,the derived signals for Signal Prime over Signal (SPOS) for eachwavelength, and the Pulse Wave Transit Time (PWTT) for each wavelength.Note the first PPG signal maxima (901) and the second PPG signal maxima(902). FIG. 10 shows the structure of the Composite Pulse Data Set,constructed from a group of Pulse Data Sets on the basis of a definedcriteria (e.g. similar prior R-to-R, or prior-prior R-to-R duration).Note how the PPG waveforms are of duration longer than a single cardiaccycle, and are long enough to assure capture of both the first (1001)and second PPG signal maxima (1002).

FIGS. 11-13 show a preferred device implementation of the system. Thedevice block diagram shows the elements of the device/system, withmultiple wavelength LEDs (1101) and a photodiode detector (1102), andEKG input from electrodes (1103) applied to the left and right chest (orleft and right upper extremities. In preferred embodiments, signals arethen fed to a processing unit (1104) carrying out “on-chip” logic thatthen generates Composite Pulse Data Sets from raw signal. The CompositePulse Data Sets are then communicated via either wireless or directcable connection to an “off-device” display/computing unit (1105) thatprovides the user with the final data in graphical form, and therecommendations with regard to fluid status. In preferred aspects, thereis preferably also on-device storage (1106) for code as well asbuffering and packetized transfer of data. In an alternate embodiment,the processing unit simply coordinates communication of raw ECG and PPGsignal data to the external computing/display device which handles allaspects of the hydration level estimation logic. In yet anotherembodiment, all aspects of hydration level estimation are carried out bythe processing unit, including rendering of graphics and makingrecommendations with regard to fluid status. In this case, the externalcomputing/display device provides only the display function.

FIGS. 12A-D show various views of the PPG collection device. 1201 showsthe optical waveguide (in front of LEDs and detector); 1202 showsoptional incorporated EKG electrodes; 1203 shows plug-in connector sitesfor EKG lead wires to adhesive EKG electrodes (on right and left chest).

FIG. 13 shows detail of the PPG head, with an optical waveguide (1301)that abuts the LEDs and detector (1302) on the interior of the device.The optical waveguide allows for collection of PPG signals at sitesother than the finger.

FIGS. 14A and 14B depicts the device in use collecting PPG signals fromthe finger (FIG. 14A), and the outside of the upper arm (FIG. 14B). ThePPG measurement end of the device is applied to the skin in a stablefashion so that PPG measurement can be taken over the course of 1-2minutes or more. EKG electrodes are applied to the left and right sidesof the torso (or upper extremities) and connected to the plug-ins on thesmaller end of the device.

FIG. 15 shows a preferred implementation of the system/device. EKGelectrodes are applied on either side of the chest/torso (or on eitherupper extremity) and an extended PPG signal is collected. Analysis isthen done to give a pre-hydration or positional change test.Administration of intravenous (IV) fluid is the more definitive means ofinjecting fluid into the venous system, yet requires time and trainedpersonnel. Elevating the lower extremities above the level of the heartmay mobilize a liter or more of venous blood and interstitial fluid,some immediately and some in a more delayed fashion. The venous bloodand/or fluid mobilized by this method is less defined, but the techniqueis quick and easily done, and can provide the needed information bydisplacing a fairly large amount of intravascular fluid into the venacava (and pre-loading/filling the right ventricle).

The System/Device analysis is repeated as in FIG. 16 to give apost-hydration or positional change assessment. The change in operatingpoint on the surrogate Frank-Starling curve (pre- to post-hydration)determines whether to give further fluid, hold fluid, or remove fluid.

Two different scenarios are shown: the curves of 1601, wherein thegraphical position 1602 translates upward to position 1603, resulting inthe recommendation to give more fluids. An alternative output is thegraph of 1604, wherein the graphical position 1605 translates laterallybut not upwards to 1606. This situation results in a recommendation tohold further fluids (and perhaps diurese/remove fluids). The presentsystem then reveals how the cardiovascular system responded to thechallenges. This provides the information needed to decide whether tohold further fluids, give fluids, or possibly give diuretics (forcingthe kidneys to release sodium and water). The present system givesinformation regarding how far from peak intravascular status the patientis.

Returning to FIG. 1 , PPG and EKG signals are collected. PPG waveformselection is performed to screen out aberrant beats considerablydifferent than the majority of pulses, such as premature ventricularcontraction beats, an example where the cardiac contraction does changeappreciably from the beat prior.

FIG. 17 shows: EKG signal (1701); EKG R-wave peak (1702); PPG signalsegments (contained within Pulse Data Sets) (1703, 1704, 1705); SPOSsignals (contained within Pulse Data Sets) (1706, 1707, 1708); PWTTusing SPOS (also contained within Pulse Data Sets) (1709, 1710, 1711).This demonstrates selection of one of the PPG signals (in the currentimplementation red, infrared, and green are used, though the approach isnot limited to using these alone) with full time length for both PPGsignal and SPOS longer than the R-to-R duration.

The present method and system of intelligent pulse averaging countersthe effect of drift in “K” (seen in equation 1), related to absorptionfrom fixed elements in the tissue being analyzed. With averaging, somepulses will have an upward drift in K, some will have a downward drift,leaving the averaged pulse with more options for data point comparisonsacross the composite pulse width.

FIG. 18 shows more detailed plots of an arterial pulse (1801), and PPGsignals associated with this pulse (Red 1802, IR 1803, Green 1804). FIG.19 shows the SPOS of Red (FIG. 19A, curve 1901), infrared (IR) (FIG.19B, curve 1902), and green (FIG. 19C, curve 1903), derived from PPGsignals for the arterial waveform in FIG. 18A. Also noted in thecharacteristic “negative spike” of the SPOS waveform (1904).

FIG. 18-19 show how SPOS generates similar shaped curves for the LEDsignals for the different wavelengths, magnitude differing only by amultiplier that is the Σ(α*Hb) for the specific wavelength. In light ofthis, the present system includes the two novel approaches of examiningthe SPOS signal in the region of the “negative spike” to determine:

-   -   the linearity of the rising LED SPOS signal, or    -   the fit of the SPOS signal to a combination of Gaussian        derivatives and/or exponential and/or polynomial equations.

Given the similar shapes for the SPOS curves, any such fitting can beapplied to one wavelength to yield a fitted curve. Fitting to anotherwavelength only requires finding the magnitude needed to best fit thatcurve. For example, if f(t) best fits the infrared LED SPOS, then “A”needed to best fit A*f(t) to the SPOS for the red LED signal yields thearterial oxygen saturation just as with the equation 1. The differencewith the standard formulation is that this fitting is based on many moretime points (up to 50 at slower heart rates) than the two (maximum andminimum) used in the standard formulation.

FIG. 20 shows this concept using a one-sided Gaussian derivativefitting. Curve 2001 are the datapoints of a collected Composite IR SPOSsignal with a fitting window 2002 selecting out the negative SPOS“spike”. Curve 2003 shows the datapoints for the window in an expandedplot, also showing the one-sided Gaussian derivative fitted curve 2004.

The interval of the fitting window selected (the SPOS “negative spike”),or subset thereof (e.g. the rising SPOS right half of the “negativespike”) represents a unique period wherein a single dominant andcoherent physiologic event—the contraction of the left ventricle duringthe time of an open aortic valve—is clearly separate from otherconfounding physiologic features. This allows for extraction ofparameters, which can then be applied to the entire PPG sensor pulsewaveform.

Multi-Beat Complexes and Prior R-to-R and Prior-Prior R-to-RDependencies

The next step with all analyses in the present system is to createmulti-pulse dependencies. Multi-beat complexes consist of an EKG segmentwith a defined R-to-R duration, tied to PPG signals for a subsequentpulse. Currently the PPG signals consist of red, infrared, and green LEDsignals, though the approach is not limited to these wavelengths. Onemulti-beat relationship is between a set of PPG signals and theimmediately prior R-to-R duration (n−1 R-to-R duration). The secondmulti-beat relationship is between the arterial oxygen saturation for aset of PPG signals and the prior-prior R-to-R duration (n−2 R-to-Rduration). The advantage of this novel approach is that it categorizesPPG waveforms on the basis of similar ventricular filling.

The filling stage of the left heart ventricle in one cycle willcorrespond to the ventricular contraction or emptying stage in the nextcardiac cycle. Stated another way, the pre-contraction left ventricularstate will depend upon the time available to fill the left ventricleafter that last contraction. Ventricular function will therefore varyslightly beat to beat depending on the variability of the pulse length.When measuring the arterial pulse, therefore, the shape of the PPGsignal seen will be dependent on the R-to-R duration of the n−1 pulse(with the current PPG pulse taken as “n”). The knowledge of prior (n−1)R-to-R duration allows for selection of similar pulses. However, notethat pulses selected by this method are only similar to the point of theshortest pulse selected, after which point the “rolloff” of thecomposite pulse being constructed is no longer valid. Because thecomposite pulse length is limited, arterial oxygen saturation can bederived for this composite pulse, though no trend attributable to rightheart variability can be assessed.

If the analysis is of arterial oxygen saturation related to R-to-Rduration, the more important relationship is between arterial oxygensaturation for a given pulse PPG signal (pulse “n”) to the prior-priorR-to-R duration (also denoted as the n−2 R-to-R). Because the pulsetransits through the lungs prior to reaching the left heart, the resultsof the ventricular filling of the right heart will be seen in thearterial system one cardiac cycle behind the effect of ventricularfilling of the left heart.

Prior R-to-R Dependency

FIG. 21 depicts a time-correlated above/below comparison of EKG (curve2101) and PPG signals (curve 2102) showing the relationships in creationof multi-beat dependencies, which are then used to create compositewaves. An EKG signal is taken over five pulses/cardiac cycles (labelledpulses A, B, C, D, and E). As seen, the pulses are of differentduration, reflecting the reality that heart rate varies slightly overtime (with the exception of certain cases of pacing with an implantedpacemaker). The current system places PPG signals in different “bins”based on R-wave peak (2103) to R-wave peak (R-to-R) durations, withpulses in the bins being analyzed together so as to generate a compositewave representative of the bin.

In one implementation three bins are used, based on short R-to-Rduration, intermediate R-to-R duration, and long R-to-R duration—thoughit is understood that the present invention is not limited to only threecategories. It is also understood that the approach here is not limitedto the use of red, green, and infrared signals used here, but mayencompass any number of wavelengths of light as the particular situationdictates.

In the first dependency, and referring to FIG. 21 , the pulses aresorted into categories based on the length of the previously measuredpulse (and not the length of the current pulse for which the PPG signalis being measured). In this first sorting, FIG. 21 pulses B and E arecategorized on the basis of their immediately prior pulse (“n−1”) R-to-Rduration (i.e.: R-to-R duration of pulses A and pulse D). Since pulses Aand D are of intermediate duration (pulse B is a short pulse and pulse Cis a longer pulse), the PPG signal of pulse B in the 2-beat dependencyA-B and the PPG signal of pulse E in the 2-beat dependency D-E aretherefore both placed in the intermediate (corresponding to n−1 R-to-R)bin. This is the case even though pulses B and E have considerablydifferent PPG signal lengths.

Also seen in FIG. 21 is selection based on another dependency, that ofPPG signal arterial oxygen saturation with prior-prior R-to-R duration(prior-prior R-to-R duration is also denoted as the n−2 R-to-R). In thisimplementation, the PPG signal of pulses A and C are grouped together.The oxygen saturation of pulse C is related back to the R-to-R durationof pulse A. The arterial oxygen saturation of pulse F (off FIG. 21 tothe right) will have dependency on the R-to-R duration of pulse D.

Two Beat Dependency Selection and Analysis:

2-beat dependency selection for a longer train of pulses in atrialfibrillation (yielding random R-to-R duration) is shown in FIG. 22 .Signal from a two-electrode, single lead EKG (curve 2201) is plotted intemporal alignment with an infrared (IR) LED PPG signal (curve 2202). Asthe infrared wavelength has relatively equivalent absorption from venousand arterial blood, this is the better wavelength with which to selectpulses for further analysis.

FIG. 22 shows the top/bottom alignment of EKG (2201) and PPG signals(2202) showing the steps in an alternative method of generating acomposite PPG wave. The above EKG (2201) signal shows a series of pulseslabeled A through I. Each of these pulses has a different duration,though some are closer in duration than others. 2-beat dependency tiestogether two successive beats, with key features being the R-to-Rduration of the first beat, and the PPG signal of the second beat. Thisis dependency (2203) is depicted in the bracket tying together theR-to-R duration of beat “B” (2204) and the PPG signal (2205) of beat“C”. Pulses B and C are analyzed together, with the R-to-R duration of Bputting this 2-beat complex in the long prior (n−1) R-to-R “bin”. Next,pulses C and D are considered together, with the R-to-R duration of Cputting this 2-beat complex in the short prior (n−1) R-to-R bin. Next,pulses D and E are considered together, with the R-to-R duration of Dputting this 2-beat complex in the long prior (n−1) R-to-R “bin” (alongwith complex B-C). Next, pulses E and F are considered, with the R-to-Rduration of E putting this 2-beat complex in the intermediate bin. 2206points to similar R-to-R durations, putting complexes B-C and D-E, alongwith H-I in the intermediate prior (n−1) R-to-R bin. 2207 points tosimilar short R-to-R durations, putting complexes C-D, F-G, and I-J (offpage to right) into the short prior (n−1) R-to-R bin. Complex G-H (2208)goes in the long prior (n−1) R-to-R bin.

Prior-Prior R-to-R Dependency

Prior-prior R-to-R dependency selection for a longer train of pulses inatrial fibrillation (yielding random R-to-R duration) is shown in FIG.23 . Signal from a two-electrode, single lead EKG (curve 2301) isplotted in temporal alignment with an infrared (IR) LED PPG signal(curve 2302). As the infrared wavelength as relatively equivalentabsorption from venous and arterial blood, this is the better wavelengthwith which to select pulses for further analysis.

FIG. 23 shows the top/bottom alignment of EKG (2301) and PPG signals(2302) showing the steps in an alternative method of generating acomposite PPG wave. The above EKG signal 2301 shows a series of pulseslabeled A through I. Each of these pulses has a different duration,though some are closer in duration than others. 2-beat dependency tiestogether two beats separated by a third beat, with key features beingthe R-to-R duration of the first beat, and the PPG signal of the beat 2beats later. This is dependency (2303) is depicted in the bracket tyingtogether the R-to-R duration of beat “B” (2304) and the PPG signal(2305) of beat “D”. Similar dependencies are shown with brackets 2306and 2307. Pulses B and D are analyzed together, with the R-to-R durationof B putting the arterial oxygen saturation from PPG signals of pulse Din the long n−2 R-to-R “bin”. Next, pulses C and E are analyzedtogether, with the R-to-R duration of C putting the arterial oxygensaturation from PPG signals of pulse E in the short n−2 R-to-R “bin”.Next, pulses D and F are analyzed together, with the R-to-R duration ofD putting the arterial oxygen saturation from PPG signals of pulse F inthe long n−2 R-to-R “bin”. Next, pulses E and G are analyzed together,with the R-to-R duration of E putting the arterial oxygen saturationfrom PPG signals of pulse Gin the intermediate n−2 R-to-R “bin”. Next,pulses F and H are analyzed together, with the R-to-R duration of Fputting the arterial oxygen saturation from PPG signals of pulse H inthe short n−2 R-to-R “bin”. Next, pulses G and I are analyzed together,with the R-to-R duration of G putting the arterial oxygen saturationfrom PPG signals of pulse I in the long n−2 R-to-R “bin”.

Thus, complexes B-D and D-F go together in the long prior-prior (n−2)R-to-R bin (2308), along with complex G-I. 2310 points to the PPGsignals for F and I that will be considered similar using this analysis.Complexes C-E and F-H go together in the short prior-prior (n−2) R-to-Rbin (2309), along with complex I-K (off page to right).

Top-Level Block Diagram for 2-Beat Dependency

The flow for the 2-beat dependency using Pulse Data Set “n” arterialoxygen saturation (“Arterial Frac O2”) from PPG signals and theprior-prior R-to-R duration is shown in the left limb (2401) of theblock diagram of FIG. 24 ; the flow for the 2-beat dependency usingPulse Data Set “n” PPG signals and the prior R-to-R duration is shown inthe right limb (2402) of the block diagram.

R-wave peak refinement of pulse “n” is done with curve fitting andinterpolation (2403) prior to determining the n−1 R-to-R duration; thenn−2 and n−1 R-to-R duration for Pulse Data Set “n” are incorporated intoPulse Data Set “n” (2404). PPG signals are gathered, and a process ofoutlier rejection is carried out (including but not limited to datadetermined to be corrupted using accelerometer input, as well ascross-checking with multiple LED PPG sensors, 2405). Once the PPGsignals of the current Pulse Data Set have been selected, the Pulse DataSet is considered together with all available prior Pulse Data Sets andtheir PPG signals (each of which is associated with a prior (n−1) R-to-Rduration and prior-prior (n−2) R-to-R duration).

At this point two different analyses are done (2406).

In the left limb (2401), the available Pulse Data Sets are sorted byprior-prior R-to-R into short, intermediate, and long “bins”, withdynamic boundary adjustment ensuring relatively equal numbers acrossbins. After available Pulse Data Sets are allocated to the given bins,an initial Composite Pulse Data Set is constructed by summing each ofthe Pulse Data Set PPG signals together. Subsequently, a pruning loop iscarried out for each bin to out to weed out Pulse Data Sets with noisyor otherwise aberrant PPG signals that made it through the coarseroutlier rejection. For each Pulse Data Set in the bin, and for eachwavelength in the Pulse Data Set, the PWTT for the wavelength iscompared against the PWTT using SPOS for the wavelength for theComposite Pulse Data Set (aggregate of all the pulses). If the PWTT oftwo of the current three wavelengths (red, green, IR) are within acertain threshold (currently 15%) of the corresponding wavelength PWTTfrom the Composite Pulse Data Set, the Pulse Data Set is left in thecomposite. If not, the Pulse Data Set is rejected (“pruned”) and theprocess is run again with the remaining Pulse Data Sets. Pruning a PulseData Set removes it from the bin and subtracts it from the CompositePulse Data Set. If the number of Pulse Data Sets falls below a specifiedthreshold for the number in the bin (good results have been obtainedwith numbers down to 4), then an additional Pulse Data Set is addedprior to reporting any results. This algorithm is seen in FIG. 25 . Whenthe Composite Pulse Data Set PPG signals for all 3 bins has beensuccessfully pruned, an arterial oxygen saturation calculation is doneon each of the set of Composite Pulse Data Set PPG signals (2407),resulting in a trio of arterial saturation to prior-prior R-to-Rduration pairs. Finally, a line representing arterial saturation vsprior-prior R-to-R is fit to the 3 data pairs (2408).

Returning to the box at the branching point left/right (“COLLECT & SORTPULSES TO BINS”, 2406), all the available Pulse Data Sets are once againconsidered, this time with regard to the prior R-to-R duration.Following the right limb (2402), the available Pulse Data Sets aresorted by prior R-to-R into short, intermediate, and long “bins”, withdynamic boundary adjustment ensuring relatively equal numbers acrossbins. After available Pulse Data Sets are allocated to the given bins,an initial Composite Pulse Data Set is constructed by summing each ofthe PPG signals together. Subsequently, the same pruning loop carriedout on the left limb is then applied to each Pulse Data Set in each binon the right limb. A pruned Pulse Data Set is removed from the bin andsubtracted it from the Composite Pulse Data Set. When the CompositePulse Data Set PPG signals for all 3 bins has been successfully pruned,the inferior-most area of the PPG curve (corresponding to the arterialpeak pulse area) is calculated for each bin (2409), yielding a trio ofpulse area to prior R-to-R duration pairs. Finally, a line representingPulse Area vs prior R-to-R is fit to the 3 pairs (2410). It is to beunderstood that SPOS signals that are based on PPG signals, and notcomposite SPOS signals, can be used for the various forms of pruning asdescribed herein.

FIG. 26 shows the derivation of the Pulse Wave Transit Time (PWTT). Thisis done using the Signal Prime Over Signal (SPOS(t)) curve for eachwavelength PPG signal(t), together with interpolation and (negative)peak refinement. FIG. 27 shows the flow diagram for the arterial oxygencalculation used in the system. Note that a one-sided Gaussianderivative is used to better fit the resultant Composite Pulse Data SetPPG signals (2701), as noted in the introduction to the detaileddescription of the invention. The system uses a fit window as seen inFIG. 20 , fitting from the infrared (IR) wavelength SPOS curve crossingfrom positive to negative down to the negative spike, then up to 15% ofthe distance from the minimum to zero (the x-axis).

Parameters A, B, C for the Gaussian curve are found using initial bestguess values (derived from time of first positive to negative crossing,time of minimum, and negative magnitude, 2703), then using a non-linearleast square error fitting (2704). Following this, the resultantGaussian is fit to the red wavelength SPOS curve (2705), allowing onlythe magnitude A to vary. An R value for A_(Red)/A_(IR) is thencalculated (2706), and put into the standard arterial oxygen saturationfraction equation (2707).

FIG. 28 shows the calculation of the pulse area (2801). This calculationuses the Composite Pulse Data Set PPG signal for the infrared (IR)wavelength. Because the absorption coefficients for oxygenatedhemoglobin and deoxygenated hemoglobin are nearly the same for IR, theIR PPG signal corresponds to the total blood flow better than the redwavelength. Calculating the area at the bottom of the IR curve(corresponding to the arterial pulse peak) thus measures how rounded thearterial pulse is at the peak. Various criteria can be used, though thecurrent criteria uses the area formed by the curve below 3/8 of themaximum—minimum IR PPG value.

System Operation:

Operational alternative options are presented in the various exemplaryembodiments of the present system, below. It is to be understood thatthe present system can be embodied in any of the systems describedherein, and that the present system is not limited solely to the variousexemplary embodiments described below:

FIG. 29 illustrates use of a chest strap (2901) across the chest, withincorporated electrodes (2902) contacting the left and right chest, andLED device with detector (2903).

FIG. 30 illustrates the cross section of a chest strap (3001) across thechest, with incorporated electrodes (3002) contacting the left and rightchest, and LED device with detector (3003).

FIG. 31 illustrates the use of a bicep strap (3101), with incorporatedelectrode (3102) and LED device with detector (3103). A second electrodepiggybacks off existing telemetry wiring (3104).

FIG. 32 illustrates a cross section of a bicep strap (3201), withincorporated electrode (3202) and LED device with detector (3203). Asecond electrode piggybacks off existing telemetry wiring (3204).

An advantage of a chest or arm strap or band is that the band/strapprovides a normal force on the LED of the PPG sensor to get a goodsignal off the chest wall. In aspects where a chest or arm strap isused, optional “traction” may also be provided on the inside of thestrap, similar to the silicone/adhesive bead that is found on the insideof standard bike shorts to keep the legs from riding up.

Appendix A: Cardiovascular Physiology Background

Cardiovascular health is essential to overall health and optimalhydration only makes sense within the context of the cardiac function ofthe given patient. There are many aspects to this optimal functioning,and a full description is beyond the scope of the background neededhere. However, an essential part of that optimal functioning is theability to simultaneously maintain the circumstances of minimalinterstitial fluid within the lungs, and high arterial flow to thecapillary networks of the body. While some tissues and organs have adegree of reserve in the event of temporary decrease in perfusion, theheart, brain, and kidneys do not. Adequate arterial pressure and volume(the result of ventricular contraction, or systole) is needed to deliveragainst gravity to the brain, and provide sufficient pressure for the“coffee filters” of the kidney to remove waste products. The balance ismaintained by keeping the pulmonary system (the portion of thecirculatory system between the right heart and the left heart, whereinblood flow through the lungs) pressures low relative to systemicarterial (the circulatory system supplying the body with oxygen gatheredfrom the lungs) pressures. Drastically simplifying the situation, thelungs must be kept “dry” while the kidneys are kept “wet”, all whilemaintaining adequate intravascular fluid needed to keep the flow ofnutrients to tissues and removal of wastes from tissues. In normalhealth the greater concern is too little intravascular fluid than toomuch. Low blood pressure, high heart rate, lightheadedness, anddeclining and darkening urine output can be a tip off to a low volumestate. Even in the state of healthy organs, additional information maysometimes be useful, as when an individual is approaching a state ofdangerous dehydration unawares; as when an individual is underanesthesia and/or post-surgical, or in the setting of acute trauma.

However, in the situation of impaired cardiovascular function, orreduced ability to retain arterial blood due to leaky capillaries(malnutrition, acute sepsis), the risk of intravascular dehydration iscomplicated by the increased problem of excess body fluid, especiallywhen that fluid builds up in the lungs. One can view the situation ofthe lungs much like a boat in water. Any boat, no matter how sound, willaccumulate water, and needs bailing on a regular basis. When the leftheart is functioning normally, it provides this needed bailing action;when left heart function is impaired for any reason, interstitial lungfluid begins to rise.

Cardiac output is the product of how much blood is ejected from theheart each beat (stroke volume) times the number of beats per minute.However, the stroke volume in turn is dependent on a number of factors(adrenergic state, prior heart insults, intravascular fluid status,etc.), including the heart rate in certain regimes.

Optimal function of the heart requires a balance between relaxation(diastole) of the heart muscle and contraction (systole). TheFrank-Starling curve (FIG. 3 ) describes the relationship between heartoutput that occurs during systole (contraction) and the filling of theheart that occurs in diastole (relaxation). As seen in the figure, evenwith normal function there is a point of peak output; however, withheart pump failure the point beyond which more end-diastolic(end-relaxation), pre-systolic (pre-contraction) filling yieldsdiminishing returns (dashed line) is more easily encountered. Prior tothat point, though, more fluid is better. The clinical problem isknowing how close to the peak one is, because the scenario is differentdepending on the curve (e.g. normal versus heart failure).

Venous return to the heart is generally harder to measure. Modeling thecirculatory system as a closed system have traditionally (and somewhatconfusingly) yielded curves of venous return as in FIG. 33 . Thesecurves have used right atrial pressure (the clinically measurablequantity) as the x-axis overlaid with Frank-Starling curves (3301, 3302,3303) using left ventricular filling/distension (the clinicallymeasurable quantity) as the x-axis. Venous return curves (3304, 3305,3306) are shown. The healthy, at rest situation is represented by theFrank-Starling curve 3301 and venous return curve 3304. Venous returncurves have a peak magnitude equal to that of the Frank-Starling curve.This is due to the requirement that, on average, there must beconservation of fluid. The intersection of the two curves (3307)identifies the cardiovascular state in resting normal health, much as asales-price point on a graph in microeconomics is identified by theintersection of supply and demand curves. About this point,perturbations of left ventricular output will occur with variation incardiac relaxation times (and thus filling time and thus end-diastolicvolume) leading up to left ventricular contraction. In doing so, theventricle will traverse up and down the Frank-Starling curve 3301 aboutthe equilibrium/operating point 3307. Venous return will alsooscillate/vary, along the venous return curve 3304, oscillating aboutthe equilibrium/operating point as well. This then will form the basisof the cardiovascular assessment made possible by this system.

Also seen in FIG. 33 are the Frank-Starling curve 3302 and venous returncurve 3305 reflecting health exercise (increase in inotropy, ormyocardial contractility from baseline, and decreased pulmonary vascularresistance from baseline). A new operating point 3308 results. Alsoshown in the situation of physiologic stress, with decreased myocardialcontractility seen by a lowered Frank-Starling curve 3303 and acorresponding new venous return curve 3306. This results in a newoperating point with decreased cardiac output (3309).

When lung interstitial fluid increases, it interferes with the abilityof hemoglobin in red blood cells to load up with oxygen, and for carbondioxide to diffuse out of the blood traversing the capillariessurrounding the alveoli, the end air sacs in the lungs where gasexchange occurs. Fluid can fill the alveoli, further impairing the lungfunction. As “back pressure” rises, signs of fluid overload within thelungs can be seen, such as decreased exercise tolerance, then difficultybreathing at rest. Characteristic sound of fluid within the lungs knownas “rales” can be heard on auscultation (listening with a stethoscope).Eventually the fluid backs up beyond the right heart to the level of thevenous system. Signs of increased jugular pulsations are seen in theacute phase. Tissue swelling (known as edema) is seen as the situationprogresses.

The more common cause of the above situation is that of chroniccongestive heart disease, though all these findings can and will be seenin the situation of acute onset such as heart attack, acute pulmonarythrombosis, acute heart failure from sepsis or shock, or acutemyocarditis, now recognized as a not uncommon complication of Covid-19.The problem for management is that the need to support adequateintraarterial volume for the brain and kidneys remains despite thedysfunction affecting the lungs and oxygenation. When this volume fallsprecipitously, the body knows to protect the brain, and therefore thekidneys are left vulnerable. Thus, the clinical situation often comesdown to finding the balance between withholding fluid (or forcing fluidoff the body) to protect the lungs versus giving fluid to support thekidneys. The additional wrinkle is that while excess fluid on the lungscauses sometimes severe discomfort, and the symptoms of kidney acutefailure are usually not noxious, the mortality of acute kidney failure(AKI or acute kidney injury) is far higher than acute pulmonary edema.The result is that far too many doctors and nurses under-resuscitate inthe setting of complicated or conflicting findings; meaning that theychoose to protect the lungs instead of the kidneys when they shouldactually favor the kidneys due to the larger but invisible danger kidneyinjury represents.

The most accessible clinical tools by which to assess cardiovascularstatus are: clinical findings (presence/absence of edema, crackles onlung examination, elevated venous pulsations, etc.), patient symptoms,EKG, vital signs (blood pressure, heart rate, respiratory rate,temperature), oxygen saturation by standard oximetry, and urine output.Less accessible but still available tools are central venous ultrasound(often more available in the intensive care unit or emergencydepartment) and echocardiogram—though the latter often takes hours to beordered, done, and then interpreted. It also requires the wheeling of adesktop-sized monitor to the bedside and a technician to carry out the30 to 45 minute test, as well as a cooperative patient. Andechocardiography cannot be done on a prone (lying face down) patient(nor can central venous ultrasound). Prone positioning is used inrespiratory failure to better aerate the posterior aspects of the lungs,though in such patients (often on ventilators) there is no access to theanterior chest. Neither system (echocardiogram or central venousultrasound) is likely to be used on a patient in infection isolation. Anechocardiogram additionally costs $800-1200 or more, and as noted aboveis not point-of-care (central venous ultrasound is billed at around$500). The Cheetah Nicom® Starling system made by Cheetah Medical ofNewton Center, Massachusetts, is newer, less cumbersome, but stillrequires significant operator training, and is both expensive and nothandheld. Data with the Cheetah Nicom® Starling system does seemencouraging, though, and validates that better knowledge ofintravascular fluid status saves lives and money.

The best information regarding cardiac function and intravascular fluidis obtained with a Swan-Ganz catheter. This is a catheter inserted intothe jugular or subclavian vein and threaded through the right heart intothe lungs, but requires a minor surgical procedure to be placed,introduces serious risk of bleeding and infection. Moreover, there isthe risk of “dropping a lung” if the lung cavity is punctured. Linesepsis (bacterial blood stream infection related to catheter placement)introduces significant risk and mortality. And catheter placement iscostly ($400+ placement, then additional RN costs with monitoring).Since a study in the late 1990s linked use of Swan-Ganz catheters toincreased mortality, even if it produced valuable clinical information,use of this catheter has been limited.

Other options such as arterial catheters to directly monitor thearterial pressure wave, or central venous pressure monitors share manyof the same risks as the Swan-Ganz catheter.

What is claimed is:
 1. A system for assessing intra-arterial fluidvolume, comprising: (a) a device positionable against a person's skin;(b) at least one PPG sensor mounted on the device for measuring theperson's PPG signal at multiple wavelengths of light; (c) a plurality ofelectrodes for measuring the person's EKG signal; (d) a computer logicsystem for receiving and analyzing the PPG signal and the EKG signal,wherein the computer logic system further comprises: (i) a system foridentifying cardiac cycles in the EKG signal; (ii) a system forsegmenting the PPG signal into a series of PPG signal segments basedupon features in the identified cardiac cycles, (iii) a system forsorting the PPG signal segments into a plurality of bins, wherein afirst set of PPG signal segments are sorted into a first plurality ofbins based upon a similarity in durations of prior R-to-R cardiaccycles, and a second set of PPG signal segments are sorted into a secondplurality of bins based upon a similarity in durations of prior-priorR-to-R cardiac cycles, (iv) a system for generating a composite signalfor each of the first and second pluralities of bins comprising a systemfor summing or averaging the PPG signal segments in the bin, (v) asystem for generating a composite Signal Prime Over Signal (SPOS) foreach of the composite signals comprising a system for calculating aderivative of the composite signal and normalizing the derivative of thecomposite signal by the composite signal itself, and (vi) a system formeasuring a person's relative hydration level by detecting a change inthe shape of at least one of the composite SPOS generated from the firstplurality of bins and the composite SPOS generated from the secondplurality of bins; and (vii) a system for outputting to a display devicea treatment recommendation based on the person's relative hydrationlevel, wherein the treatment recommendation is at least of a positionalchange, giving further fluid, holding fluid, removing fluid, or givingdiuretics.
 2. The system of claim 1, wherein the computer logic systemfurther comprises: (viii) a system for determining Pulse Wave TransitTime by determining the time interval between the onset of an R-wavecomplex in the cardiac cycle and the occurrence of a shape feature inthe composite SPOS.
 3. The system of claim 2, wherein the shape featurein the composite SPOS is a minimum of the composite SPOS.
 4. The systemof claim 1, wherein the system for generating a composite signal foreach of the sets comprises a system for iteratively re-calculating thecomposite signal, by: comparing a SPOS of each of the PPG signalsegments used to calculate the composite signal against the compositeSPOS of the calculated composite signal, removing outlier PPG signalsegments, re-calculating the composite signal with the outlier PPGsignal segments removed, and repeating the iteration until there are nomore outlier PPG signal segments.
 5. The system of claim 4, whereinoutlier PPG signal segments are identified by comparing PPG signalsegments measured at different wavelengths of light against thecalculated composite signal.
 6. The system of claim 4, wherein comparinga SPOS of each of the PPG signal segments used to calculate thecomposite signal against the composite SPOS of the calculated compositesignal comprises: comparing PWTT values for the PPG signal segmentsagainst a PWTT value for the calculated composite signal.
 7. The systemof claim 1, wherein the system for measuring a person's hydration levelis configured to: calculate a first relationship representing leftventricular output with arterial pulse shape as a function of priorR-to-R, the first relationship being based upon values of the compositesignals generated from the first set of PPG signal segments, calculate asecond relationship representing venous return with arterial hemoglobinoxygen saturation as a function of prior-prior R-to-R, the secondrelationship being based upon the composite signals generated from thesecond set of PPG signal segments, and compare the first and secondrelationships as a metric of a person's relative hydration level.
 8. Thesystem of claim 1, wherein the system for measuring a person's hydrationlevel detects the change in the shape of a composite SPOS signalmeasured at an infrared wavelength of light.
 9. The system of claim 8,wherein detecting the change in the shape of the composite signalmeasured at the infrared wavelength of light comprises correlatingintra-arterial fluid volume to an area under a curve representative ofthe composite signal measured at the infrared wavelength of light. 10.The system of claim 1, wherein the device is a hand-held device with theat least one PPG sensor mounted thereon and a plurality of electrodewires extending therefrom.
 11. The system of claim 10, wherein thedevice is a hand-held device with the at least one PPG sensor mountedthereon and at least one of the plurality of electrodes mounted thereon.12. The system of claim 10, wherein an optical waveguide is interposedbetween the at least one PPG sensor on the device and the person's skin.13. The system of claim 1, wherein the device is positioned within astrap or band disposed around the person's chest, back, forehead, orlimb such that the at least one PPG sensor and the plurality ofelectrodes are disposed within the strap or band disposed around theperson's chest, back, forehead or limb.
 14. The system of claim 1,wherein the device is a patch with the at least one PPG sensor and atleast one of the plurality of electrodes positioned therein.
 15. Thesystem of claim 1, wherein the computer logic system is positionedwithin the device such that the composite signals are generated withinthe device, and wherein the system for measuring intra-arterial fluidvolume comprises: a data transmission system for transmitting one orboth of: the composite signals to a remote computer system for analysis,or measured PPG and EKG signals to a remote computer system foranalysis.
 16. The system of claim 1, wherein the system for generating acomposite signal for the plurality of bins in each of the first andsecond sets of PPG signal segments comprises a system for removingaberrant PPG signal segments from the calculation of the compositesignal.
 17. A system for assessing intra-arterial fluid volume,comprising: (a) a device positionable against a person's skin; (b) atleast one PPG sensor mounted on the device for measuring the person'sPPG signal at multiple wavelengths of light; (c) a plurality ofelectrodes for measuring the person's EKG signal; (d) a computer logicsystem for receiving and analyzing the PPG signal and the EKG signal,wherein the computer logic system further comprises: (i) a system foridentifying cardiac cycles in the EKG signal; (ii) a system forsegmenting the PPG signal into a series of PPG signal segments basedupon features in the identified cardiac cycles, (iii) a system forsorting the PPG signal segments into a plurality of bins, wherein afirst set of PPG signal segments are sorted into a first plurality ofbins based upon similarity in durations of prior R-to-R cardiac cycles,and a second set of PPG signal segments are sorted into a secondplurality of bins based upon similarity in durations of prior-priorR-to-R cardiac cycles, (iv) a system for generating a composite signalfor each of the first and second pluralities of bins comprising a systemfor removing aberrant PPG signal segments from the calculation of thecomposite signal, wherein the system for removing aberrant PPG signalsegments from the calculation of the composite signal comprises a systemfor iteratively re-calculating the composite signal, by: comparing aSPOS of each of the PPG signal segments used to calculate a compositesignal against a SPOS of the calculated composite signal, removingoutlier PPG signal segments, re-calculating the composite signal withthe outlier PPG signal segments removed, and repeating the iterationuntil there are no more outlier PPG signal segments; (v) a system formeasuring a person's relative hydration level by detecting a change inthe shape of at least one of the composite signals generated from thefirst plurality of bins and the composite signals generated from thesecond plurality of bins, and (vi) a system for outputting to a displaydevice a treatment recommendation based on the person's relativehydration level, wherein the treatment recommendation is at least of apositional change, giving further fluid, holding fluid, removing fluid,or giving diuretics.
 18. The system of claim 17, wherein outlier PPGsignal segments are identified by comparing PPG signal segments measuredat different wavelengths of light against the calculated compositesignal.